# METHODOLOGY — Economic Crimes by Category in Latin America (2020-2023)

**Dataset DOI:** https://doi.org/10.7910/DVN/8FXZOJ
**Version:** V2
**Date:** 2026-03-20

---

## 1. Research Objective

To compile a standardized, comparative dataset of economic crime statistics across 20 Latin American countries for 2020-2023, covering five major categories: corruption, tax evasion, money laundering, fraud/scams, and organized economic crime.

---

## 2. Data Collection Protocol

### 2.1 AI-Assisted Search Strategy

Data were collected in February 2026 using AI-assisted searches via Perplexity AI:

1. Query formulation: Country-specific and regional queries for each crime category
2. Source identification: Retrieval from official international databases
3. Cross-validation: All values cross-validated against primary source documents
4. Data entry: Validated values manually entered into structured tabular format

### 2.2 Primary Sources

| Source | Organization | Variables Extracted |
|--------|-------------|---------------------|
| Corruption Perceptions Index (CPI) | Transparency International | cpi_score (2020-2023) |
| Illicit Financial Flows reports | Global Financial Integrity (GFI) | tax_evasion_gdp_pct |
| Mutual Evaluation Reports | FATF/GAFI | ml_risk_level |
| Economic Crime Survey | PwC | fraud_companies_pct |
| Global Study on Homicide | UNODC | homicide_rate |
| Regional economic analyses | Inter-American Development Bank (IDB) | tax_evasion_gdp_pct |

---

## 3. Inclusion/Exclusion Criteria

### Inclusion
- 20 Latin American nations with available data
- Period: 2020-2023 (post-pandemic economic crime data)
- Categories: Corruption, Tax Evasion, Money Laundering, Fraud/Scams, Organized Crime

### Exclusion
- Data prior to 2020 (different economic context)
- Caribbean island nations not typically included in LAC economic crime reports
- Data from non-peer-reviewed or non-institutional sources

---

## 4. Data Processing

1. Raw data extracted from AI-assisted search results
2. Standardized to common units and scales per variable
3. Country names harmonized to standard English nomenclature
4. Missing values flagged; regional averages applied where indicated
5. Qualitative risk levels converted to ordered categorical variables

---

## 5. Limitations

- Perception-based indices: CPI measures perception, not actual corruption
- Estimation uncertainty: Tax evasion estimates involve significant methodological assumptions
- Data gaps: Some countries lack annual data for all categories; regional averages used
- AI-assisted collection: Despite cross-validation, selection bias may exist
- Venezuela: Limited official reporting reduces data reliability
- Temporal coverage: 2020-2023 data reflects post-COVID economic disruptions

---

## 6. Reproducibility

All processing steps are documented in:
- analysis_script.R — R analysis and visualization script
- economic_crimes_latam_analysis.ipynb — Python Jupyter Notebook

Primary sources listed in references.tab.